Source code for pandas_ta.momentum.td_seq

# -*- coding: utf-8 -*-
# import numpy as np
from numpy import where as npWhere
from pandas import DataFrame, Series
from pandas_ta.utils import get_offset, verify_series

[docs]def td_seq(close, asint=None, offset=None, **kwargs): """Indicator: Tom Demark Sequential (TD_SEQ)""" # Validate arguments close = verify_series(close) offset = get_offset(offset) asint = asint if isinstance(asint, bool) else False show_all = kwargs.setdefault("show_all", True) def true_sequence_count(series: Series): index = series.where(series == False).last_valid_index() if index is None: return series.count() else: s = series[series.index > index] return s.count() def calc_td(series: Series, direction: str, show_all: bool): td_bool = series.diff(4) > 0 if direction=="up" else series.diff(4) < 0 td_num = npWhere( td_bool, td_bool.rolling(13, min_periods=0).apply(true_sequence_count), 0 ) td_num = Series(td_num) if show_all: td_num = td_num.mask(td_num == 0) else: td_num = td_num.mask(~td_num.between(6,9)) return td_num up_seq = calc_td(close, "up", show_all) down_seq = calc_td(close, "down", show_all) if asint: if up_seq.hasnans and down_seq.hasnans: up_seq.fillna(0, inplace=True) down_seq.fillna(0, inplace=True) up_seq = up_seq.astype(int) down_seq = down_seq.astype(int) # Offset if offset != 0: up_seq = up_seq.shift(offset) down_seq = down_seq.shift(offset) # Handle fills if "fillna" in kwargs: up_seq.fillna(kwargs["fillna"], inplace=True) down_seq.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: up_seq.fillna(method=kwargs["fill_method"], inplace=True) down_seq.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"TD_SEQ_UPa" if show_all else f"TD_SEQ_UP" = f"TD_SEQ_DNa" if show_all else f"TD_SEQ_DN" up_seq.category = down_seq.category = "momentum" # Prepare Dataframe to return df = DataFrame({ up_seq, down_seq}) = "TD_SEQ" df.category = up_seq.category return df
td_seq.__doc__ = \ """TD Sequential (TD_SEQ) Tom DeMark's Sequential indicator attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Sources: Calculation: Compare current close price with 4 days ago price, up to 13 days. For the consecutive ascending or descending price sequence, display 6th to 9th day value. Args: close (pd.Series): Series of 'close's asint (bool): If True, fillnas with 0 and change type to int. Default: False offset (int): How many periods to offset the result. Default: 0 Kwargs: show_all (bool): Show 1 - 13. If set to False, show 6 - 9. Default: True fillna (value, optional): pd.DataFrame.fillna(value) Returns: pd.DataFrame: New feature generated. """